Vibe Spaces for Creatively Connecting and Expressing Visual Concepts
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Abstract (arXiv)
Creating new visual concepts often requires connecting distinct ideas through their most relevant shared attributes -- their vibe. We introduce Vibe Blending, a novel task for generating coherent and meaningful hybrids that reveals these shared attributes between images. Achieving such blends is challenging for current methods, which struggle to identify and traverse nonlinear paths linking distant concepts in latent space. We propose Vibe Space, a hierarchical graph manifold that learns low-dimensional geodesics in feature spaces like CLIP, enabling smooth and semantically consistent transitions between concepts. To evaluate creative quality, we design a cognitively inspired framework combining human judgments, LLM reasoning, and a geometric path-based difficulty score. We find that Vibe Space produces blends that humans consistently rate as more creative and coherent than current methods.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 26 of its 47 references that are in the map. First posted on arXiv 16 Dec 2025.
Builds on
26 of 47 references.
References · 26
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
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- 2022Diffusion Models already have a Semantic Latent Spacecited by 328
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- 2024FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Modelscited by 217
- 2023Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Modelscited by 153
- 2023Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometrycited by 125
14 more
- 2023DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphingcited by 67
- 2023Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Modelscited by 49
- 2023Norm-guided latent space exploration for text-to-image generationcited by 43
- 2024Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directionscited by 41
- 2023Interpolating between Images with Diffusion Modelscited by 38
- 2024Interpreting the Weight Space of Customized Diffusion Modelscited by 36
- 2024AID: Attention Interpolation of Text-to-Image Diffusioncited by 25
- 2023IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Modelscited by 21
- 2025FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Modelcited by 17
- 2018Totally Looks Like - How Humans Compare, Compared to Machinescited by 12
- 2025Probability Density Geodesics in Image Diffusion Latent Spacecited by 9
- 2025Probing and Inducing Combinational Creativity in Vision-Language Modelscited by 6
- 2025Creative Blends of Visual Conceptscited by 5
- 2024Investigating Conceptual Blending of a Diffusion Model for Improving Nonword-to-Image Generationcited by 2
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Citing papers per year: 2026: 3.
3 papers.
Similar, not cited
Close in wording, no citation either way
- 2026Less Is More: Balancing Positive and Negative Space in Visual Concept Blending
- 2026Inspiration Seeds: Learning Non-Literal Visual Combinations for Generative Exploration
- 2025Blending Concepts with Text-to-Image Diffusion Models
- 2023Concept Decomposition for Visual Exploration and Inspiration
- 2024How to Blend Concepts in Diffusion Models
- 2025Imagine for Me: Creative Conceptual Blending of Real Images and Text via Blended Attention
- 2026Creative Image Generation with Diffusion Models
- 2025Latent Diffusion : Multi-Dimension Stable Diffusion Latent Space Explorer